LLM

• Written by Charan Akiri, with help from Dylan Raithel. • TL;DR We built a centralized LLM Guardrails Service at Reddit to detect & block malicious & unsafe inputs-including promp

• Authors: Andrew Garrett, Md Mansurul Bhuiyan With 10s of thousands of new posts on Reddit each day, identifying content that is simultaneously timely, newsworthy, and engaging pr

• SpellVault’s evolution: Beyond LLM apps, towards the agentic future Introduction At Grab, innovation isn’t just about building new features; it’s about evolving our platforms to

• We’re releasingSlither-MCP, a new tool that augments LLMs with Slither’s unmatched static analysis engine. • Slither-MCP benefits virtually every use case for LLMs by exposing Sl

• How we built a custom vision LLM to improve document processing at Grab Introduction In the world of digital services, accurate extraction of information from user-submitted docu

• Istio now supports Gateway API Inference Extension, enabling model‑aware, LoRA‑aware routing for AI workloads. • AI inference requests can last seconds to minutes, making routing

• LLMs power new apps but prompt injection is top OWASP threat. • Attack injects malicious instructions into untrusted data, overriding trusted prompts. • Real-world examples: Yelp

• Yelp integrates LLMs to interpret search queries, improving intent detection for millions of daily searches. • The team tackled spelling correction, segmentation, canonicalizatio

• Anthology conditions LLMs with detailed backstories to create consistent virtual personas. • Uses naturalistic life narratives to represent diverse human values and experiences.

• Researchers tested jailbreak via Scots Gaelic translation, initially replicating 43% success claim. • GPT-4 responded with bomb instructions in Gaelic, but full output differed f

• Authors propose a new preprint exploring math behind large language models. • Question: how to model transition from probability distributions on text to syntax and semantics. •